Feasibility of Cancer Detection Through X-Ray Diffraction of Human Nails.
Authors
Affiliations (11)
Affiliations (11)
- Deutsches Elektronen-Synchrotron DESY, 22607 Hamburg, Germany.
- Bragg Analytics, Inc., 1900 S. Norfolk Street, #350, San Mateo, CA 94403, USA.
- Matur UK Ltd., 5 New Street Square, London EC4A 3TW, UK.
- Scientific Center of Nanotechnology and Functional Nanomaterials, Buketov Karaganda National Research University, 28 University Street, Karaganda 100028, Kazakhstan.
- Surgery Department, Regional Oncology Center of Karaganda, 189 Amanzholov Street, Karaganda 100019, Kazakhstan.
- Department of Surgical Diseases, Karaganda Medical University, 40 Gogol Street, Karaganda 100012, Kazakhstan.
- Department of Science and Commercialization, Kazakh National University of Sports, 15 Karkaraly Street, Astana 010000, Kazakhstan.
- Biology-Geography Faculty, Buketov Karaganda National Research University, 28 University Street, Karaganda 100028, Kazakhstan.
- Department of Ecology and Assessment, Karaganda University of Kazpotrebsoyuz, 9 Academic Street, Karaganda 100009, Kazakhstan.
- Faculty of Engineering and Applied Sciences, Shrivenham Campus, Cranfield University, Swindon SN6 8LA, UK.
- Physics Department, Queens College, City University of New York, 65-30 Kissena Blvd, Flushing, NY 11367, USA.
Abstract
<b>Background/Objectives</b>: Establishing biomarkers is crucial for early cancer detection. Structural biomarkers can be revealed by noninvasive X-ray diffraction. We applied this approach to human nails and observed structural changes in the major nail tissue component, keratin, which coincided with a cancer diagnosis. <b>Methods</b>: X-ray diffraction patterns were obtained from 230 nail samples (control/cancer split: 92/138) belonging to 133 patients (48/85) in wide (WAXS) and small (SAXS) ranges of scattering angles. For both ranges, we obtained one-dimensional scattering profiles in meridional and equatorial directions. We examined these profiles using machine-learning techniques such as logistic regression and linear discriminant analysis. <b>Results</b>: We obtained good classification metrics that allow us to distinguish participants with cancer from controls. The results are similar for both machine-learning methods. Combining all four profiles (WAXS meridional, WAXS equatorial, SAXS meridional, and SAXS equatorial) produced a sensitivity of 0.753 and a specificity of 0.771 for the Linear Discriminant Analysis at the Youden point. For the limited set of patients of the same age, we showed that the patterns can still be distinguished, supporting our hypothesis that the observed aberrations are cancer-induced. <b>Conclusions</b>: Our exploratory research demonstrated the feasibility of cancer detection using the X-ray diffraction of human nails.